To integrate AI into your company without failing: start with ONE high-value, low-risk use case — usually search over your documents (RAG), automating a repetitive process (n8n + AI), or an internal copilot. Ship a pilot in a few weeks, measure, then expand. The trap is trying to put AI everywhere at once.
The 4 use cases that actually work
RAG (search over your documents): an assistant that answers from YOUR content with sources. The most profitable and least risky place to start.
Process automation (n8n + AI): connect your tools and insert AI where it saves time — triage, extraction, drafting, routing.
Agents / internal copilots: a business assistant that runs bounded tasks (prepare a quote, qualify a lead, summarize).
Assisted generation: produce drafts (emails, minutes, content) that a human approves.
Where to start
Pick a high-ROI, low-risk case on data you already have. Frame the expected outcome, ship a usable pilot, measure, then expand. A forward-deployed integrator who ships to production avoids the tunnel effect.
Pitfalls to avoid
Hallucination: neutralize with RAG (sourced answers) and guardrails, not a bare LLM.
Privacy: controlled architecture, zero-retention providers if needed; your data isn't used to train third-party models.
The big bang: start with a single case instead of transforming everything at once.
No measurement: without a gain metric, you can't justify expanding.
How long, which team
A first usable pilot ships in weeks, not months. You don't need a big data team to start: an integrator who masters product, AI and deployment is enough for the first case.
Which need → which AI solution
| Business need | AI solution | Concrete example (DirtyLab) |
|---|---|---|
| Find info in internal documents | RAG (search + sourced answers) | AG Avocats — legal RAG + dashboards |
| Automate a repetitive process | n8n + AI (extraction, triage, drafting) | Process audits & pipelines (SMEs, independents) |
| Remove field double-entry | Mobile-first app + structuring | Industrial SME — paper form → app |
| Recommend / match | Contextual model | VYBZ — recommendation by 'vibe' |
FAQ
Where do I concretely start?
With a single high-ROI, low-risk case on data you already have — usually a RAG over your documents or automating a repetitive process.
How long until a first result?
A usable pilot ships in a few weeks. Measure the gain before expanding.
Do I need a big data team?
Not to start. An integrator who ships to production is enough for the first case; your team upskills afterwards.
Is my data private?
Yes with a controlled architecture: RAG over your own data, zero-retention providers if needed. Your data doesn't train third-party models.
RAG or fine-tuning?
RAG first: simpler, always up to date, cheaper, and it cites sources. Fine-tuning is only worth it in specific cases.
An AI use case in mind? Erwan André (DirtyLab) frames it, builds it and ships it to production.